(1) Field of the Invention
The invention relates to a vehicle control system for autonomously piloting a vehicle utilizing a multi-objective optimization method that evaluates a plurality of objective functions to determine the best decision variables satisfying those objectives.
(2) Description of the Prior Art
The mission assigned to an underwater vehicle strongly shapes the navigation complexity and criteria for success. While many problems are similar between commercial and military AUVs, there is a stronger emphasis in military vehicles in reasoning about other nearby moving vessels. Military AUVs (more commonly referred to as unmanned underwater vehicles (UUVs)) are typically designed to operate in congested coastal situations, where a near-collision or mere detection by another vessel can jeopardize the AUV. The scenario considered in this application therefore centers around the need to consider preferred relative positions to a moving contact, while simultaneously transiting to a destination as quickly and directly as possible. By “preferred relative position”, we primarily mean collision avoidance, but use this term also in reference to other objectives related to relative position. These include the refinement of a solution on a detected contact, the avoidance of detection by another contact, and the achievement of an optimal tactical position should an engagement begin with the contact.
Other researchers have submitted material in the art of autonomous vehicle navigation.
Rosenblatt in “DAMN: A Distributed Architecture for Mobile Navigation,” PhD thesis, Carnegie Mellon University, 1997 teaches the use of behavior functions voting on a single decision variable with limited variation. Multiple behavior functions provide votes for an action having five different possibilities. Additional control is provided by having a mode manager that dynamically adjusts the weights of the behavior functions. While Rosenblatt indicates that decision variables for turns and speed are desirable, coupling of these two decision variables into a single control system at the same time is not provided.
Riekki in “Reactive Task Execution of a Mobile Robot,” PhD Thesis, University of Oulu, 1999, teaches action maps for each behavior that can be combined to guide a vehicle using multiple decision variables. Riekki discloses action maps for obstacle avoidance and velocity.
These publications fail to teach the use of multiple decision variables having large numbers of values. No method is taught for determining a course of action in real time from multiple behavior functions. Furthermore, these publications do not teach the use of action duration as a decision variable.